" The chemical engineering literature is dominated by physical and (bio)-chemical processes that exhibit complex nonlinear behavior, and as a consequence, the associated requirements of their analysis, optimization, control and monitoring pose considerable challenges in the face of emerging competitive pressures on the chemical, petrochemical and pharmaceutical industries. The above operational requirements are now increasingly imposed on processes that exhibit inherently nonlinear behavior over a wide range of operating conditions, rendering the employment of linear process control and monitoring methods rather inadequate. At the same time, increased research efforts are now concentrated on the development of new process control and supervisory systems that could be digitally implemented with the aid of powerful computer software codes. In particular, it is widely recognized that the important objective of process performance reliability can be met through a comprehensive framework for process control and monitoring. From: (i) a process safety point of view, the more reliable the process control and monitoring scheme employed and the earlier the detection of an operationally hazardous problem, the greater the intervening power of the process engineering team to correct it and restore operational order (ii) a product quality point of view, the earlier detection of an operational problem might prevent the unnecessary production of o-spec products, and subsequently minimize cost. The present work proposes a new methodological perspective and a novel set of systematic analytical tools aiming at the synthesis and tuning of well-performing digital controllers and the development of monitoring algorithms for nonlinear processes. In particular, the main thematic and research axis traced are: (i) The systematic integrated synthesis and tuning of advanced model-based digital controllers using techniques conceptually inspired by Zubov’s advanced stability theory. (ii) The rigorous quantitative characterization and monitoring of the asymptotic behavior of complex nonlinear processes using the notion of invariant manifolds and functional equations theory. (iii) The systematic design of nonlinear state observer-based process monitoring systems to accurately reconstruct unmeasurable process variables in the presence of time-scale multiplicity. (iv) The design of robust nonlinear digital observers for chemical reaction systems in the presence of model uncertainty. "

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-30Bitstream added on 2014-06-13T18:08:32Z : No. of bitstreams: 1 cruz_ced_me_bauru.pdf: 2306291 bytes, checksum: 490ef2d600c46e48d97b175d02746256 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A crescente competitividade do mercado, exigência por qualidade, padronização cada vez superior a necessidade de redução do desperdícios trazem cada vez mais a automação às indústrias. Por suas características, os processos automatizados podem ser melhorados com a utilização de métodos de controle e supervisão e, neste campo, a utilização de sensores e redes neurais artificiais têm cada vez mais destaque em pesquisa. No processo de furação, estudos relatam a aplicação bem sucedida destas técnicas na determinação do fim da vida de ferramentas, contudo, em muitas aplicações, apenas o controle do desgaste da broca não é suficiente para garantir a qualidade do produto. Diâmetro do furo usinado, rugosidade e a formação de rebarbas são alguns exemplos de importantes resultados do processo que não dependem exclusivamente da condição da ferramenta de corte, neste âmbito, estudos dedicados ao controle destas variáveis são limitados, senão inexistentes. Desta forma, este estudo foi conduzido buscando gerar uma contribuição à supervisão do processo de furação com foco na estimação do diâmetro e rugosidade do furo usinado. Utilizando um sistema multi sensores instalados em uma máquina ferramenta e corpus de prova compostos por uma liga de titânio seguida de uma liga de alumínio, registraram-se os sinais dos sensores durante o corte para variados parâmetros de usinagem. Os dados coletados serviram de entrada a uma rede neural artificial, que foi treinada com os valores de diâmetro e rugosidade medidos parte das amostras. Depois do treinamento, a rede capacitou-se a estimar os valores de diâmetro e rugosidade média a partir dos sinais coletados somados aos parâmetros de corte utilizados na concepção do furo. Os erros dos processo foram então calculados da diferença entre os valores medidos e as saídas obtidas. Os resultados demonstraram alta capacidade da rede em determinar as viariáveis desejadas / The growing market competitiveness, product quality requirements and just in time production concept is bringing every time more automation to manufacturing industries. Productivity and quality in machining process can be improved by using monitoring and controlling methods. Along the last decades, sensors and Artificial Neural Network have been successfully utilized in many drill wear monitoring systems. However, in many industrial fields, to supervise the tool wear is not enough to assure the product qualities. Roughnessm burr formation and hole diameter are some examples of important process results does not exclusively depend on the tool condition and in this area of knowledge the number of studies is limited or inexistent. Thus, this work brings a contribution on drilling process monitoring where the target was to determine the hole diameter and roughness using a multi-sensor system and artificial neural network. The speciments used were composed by a titanium alloy and aluminum alloy plates. The sensors were installed in a machine tool and the process was accomplished using several drilling parameters. The acquired sensors data were used a input in an artificial neural network which was trained with the roughness and diameter measured in some samples. After trained, the system was qualified to output the expected variables from the input signals. To calculate the errors these output values were compared with the samples measured. The results showed efficiency of the system in determining the roughness and hole diameters as the obtained errors can be considered low or neigligible for the majority of drilling industrial application

Orientador: Paulo Roberto de Aguiar / Banca: Eraldo Janinone da Silva / Banca: José Alfredo Covolan Ulson / Resumo: O monitoramento de processos de fabricação pro usinagem tem se mostrado de extrema importância nas empresas que buscam um nível de excelência em qualidade, auxiliando na melhor alocação de recursos e redução de desperdícios oriundos de peças com problemas de qualidade. Abordagens multisensoriais têm sido empregadas no monitoramento desses processos com o objetivo de utilizar os sinais no treinamento de sistemas de inteligência artificial na tarefa de indicar desvios nas ferramentas ou no produto sendo fabricado. Neste trabalho, três sistemas de inteligência artificial foram utilizados com o o objetivo de prover estimativas para o diâmetro de furos obtidos por processo de furação de precisão. Uma rede neural artificial perceptron de múltiplas camadas (RNA MLP), um sistema de inerferência adaptável neuro-fuzzy (ANFIS) e uma rede neural artificial com função de base radial (RBF) foram treinados com os dados obtidos com os sensores para estimar os diâmetros dos furos para cada material de corpo-de-prova. A definição da melhor configuração para cada sistema de inteligência artificial foi obtida através de algoritmos desenvolvidos para verificar a influência dos sinais e dos parâmetros particulares de cada sistema sobre o resultado final da estimativa. Os resultados obtidos indicam que a RNA MLP apresenta maior robutez perante a variação nos dados apresentados. O sistema ANFIS e a rede RFB mostraram que seu resultado varia acentuadamente quando há variações nos dados apresentados no treinamento, requerendo que estes sistemas sejam treinados sempre com o conjunto de dados apresentados na mesma ordem. A análise de influência dos sinais mostrou que, embora a abordagem multisensorial apresente bons resultados na rede MLP, o mesmo não se repetiu para os demais sistemas... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The supervision of manufacturing process by machining has been extremely important in companies which aim an excellence level in quality, helping on best assets allocation and waste reduction originated from pieces with quality problems. Multi-sensory approaches have been employed in the supervision of these processes aiming the use of signals in the training of artificial intelligence systems in order to indicate deviations in tools or in product being manufactured. Turning, grinding, milling and drilling have benn the target of the application of these supervision intelligence systems. In this work these artificial intelligence systems were applied in order to provide estimations for the diameters of the holes obtained by precision drilling process. A Multilayer Perceptron Neural Network (ANN MLP), and adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network with radial basis function (RBF) were trained with the data obtained from the sensors to estimate the hole diameters for each material of the test pieces. The definition of the best configuration for each artificial intelligence system was obtained through algorithms developed to verify the signals influence and particular parameters of each system concerning the final estimation result. The tests results were analyzed under three criteria: the absolute and medium errors, the system capacity of obtaining correct results for each estimation - classifying them as approved or rejected - and the error analysis regarding the necessary tolerance classes to maintain process within the limits of precision mechanics. The results obtained indicate that the ANN MLP presents higher robustness before variation in the data presented. The ANFIS system and RFB network have shown that their result vary sharply when there are data variations presented in training... (Complete abstract click electronic access below) / Mestre

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment. The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations. A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time. Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods. Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop – DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

Les défauts récurrents apparaissant dans des contextes particuliers ont un impact non négligeable sur le rendement, lors de la fabrication des noeuds technologiques nanométriques. C'est pourquoi une nouvelle méthode de contrôle du procédé de fabrication in-situ est développée, en vue d'améliorer la performance globale de l'outil de production. Elle se veut complémentaire des techniques d'analyses de défaillance classique, notamment en cas de crise. Le principe est de transformer un circuit de production en un véhicule de test en réutilisant des éléments qui le composent. Le circuit perd alors sa fonctionnalité originale au profit de fonctions de contrôle du procédé de fabrication réalisées uniquement à base de cellules logiques disponibles en grand nombre dans les circuits. Cette transformation, baptisée «échange topologique», implique la modification de certains niveaux de métallisation et nécessite la création d'un flot de conception particulier, basé sur des techniques d'Engineering Change Order (ECO). Comme plusieurs fonctions doivent pouvoir cohabiter ensemble sur un même véhicule, un système de multiplexage a été évalué. La faisabilité est montrée au travers d'un circuit de test réalisé de manière analogue à un circuit en production. Il est transformé pour l'exemple en un système intégré de contrôle d'épaisseur de diélectrique. / Recurrent defects appearing in specific contexts have a significant impact on nanoscale technology nodes manufacturing process yield. Therefore, a new in-situ process monitoring method is developed to improve the overall performance of the production tool. It is complementary to classical failure analysis techniques, especially when a yield crisis occurs. The idea is to transform a production circuit into a test vehicle by reusing its components. The circuit loses its original functionality in favor of process monitoring functions, carried out only with standard cells widely available in circuits. This transformation, called "topological exchange" involves modifying some levels of metallization and requires the creation of a particular design flow, based on Engineering Change Order (ECO) techniques. As several functions must be able to cohabit on the same vehicle, a multiplexing system is evaluated. Feasibility is shown through a test circuit designed analogously to a production circuit. It is transformed for the example into an integrated dielectric thickness control system.

A major issue within automotive Sheet Metal Forming (SMF) concerns ensuring desired output product quality and consistent process performance. This is fueled by complex physical phenomena, process fluctuations and complicated parameter correlations governing the dynamics of the production processes. The aim of the thesis is to provide a deeper understanding of the challenges and opportunities in this regard within automotive SMF. The research is conducted in collaboration with a global automotive manufacturer.  The research shows that systematic investigations using process simulation models allow exploration of the product-process parameter interdependencies and their influence on the output product quality. Furthermore, it is shown that incorporating in-line measured data within process simulation models enhance model prediction accuracy. In this regard, automating the data processing and model configuration tasks reduces the overall modelling effort. However, utilization of results from process simulations within a production line requires real-time computational performance. The research hence proposes the use of reduced process models derived from process simulations in combination with production data, i.e. a hybrid data- and model-based approach. Such a hybrid approach would benefit process performance by capturing the deviations present in the real process while also incorporating the enhanced process knowledge derived from process simulations. Bringing monitoring and control realms within the production process to interact synergistically would facilitate the realization of such a hybrid approach. The thesis presents a procedure for exploring the causal relationship between the product-process parameters and their influence on output product quality in addition to proposing an automated approach to process and configure in-line measured data for incorporation within process simulations. Furthermore, a framework for enhancing output product quality within automotive SMF is proposed. Based on the thesis findings, it can be concluded that in-line measured data combined with process simulations hold the potential to unveil the convoluted interplay of process parameters on the output product quality parameters. / <p>Related work:</p><p>1) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14412</p><p>2) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-14388</p><p>3) http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18935</p>

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Mining activities around the world are undergoing constant change and modernization owing to technological and scientific advancements. Consequently, there are frequent proposals to streamline and enhance processes in mining operations. This study deals with ore transportation in mechanized mining units and aims to optimize fleet management using the Lean Six Sigma methodology to obtain a model in this specific process. The proposed method was implemented using a Lean Six Sigma instrument known as DMAIC (Define, Measure, Analyze, Improve, and Control). The case study was applied to an underground mine located in the Huancavelica region, Peru. The simulation showed that 24% of the time in the ore transport cycle is un-productive time and the improvement potential time represents 53% of the transportation process time.